This is an introduction of the code developed for the Deep Clustering Network (DCN). Please direct your emails to Bo Yang, yang4173@umn.edu if you have troubles running the code, or find any bugs. Here is the paper: arxiv: https://arxiv.org/pdf/1610.04794v1.pdf Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos and Mingyi Hong "Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering" ============================================== Main files run_raw_mnist.py : Script to reproduce our results on raw-MNIST dataset multi_layer_km.py : Main file for defining the network, as well as various utility functions. You can start running the code by e.g. (on Ubuntu) $: ./run_raw_mnist.sh -- More documentations can be found inside each of the above files. -- ============================================== Data preparation The data file should be named like 'something.pkl.gz', i.e., it should be pickled and compressed by gzip, using python code as follow: """ with gzip.open('something.pkl.gz', 'wb') as f: cPickle.dump([train_x, train_y], f, protocol = 0) """ where train_x and train_y are numpy ndarray with shape train_x: (n_samples, n_features) train_y: (n_samples, ) ============================================== Main difference compared to previous release 1) Included the dependent files 2) Included sample data files 3) Added theano environment flag in the .sh file 4) Cleaned up the repo to exclude unnecessary files ============================================== Dependencies Theano scikit-learn numpy scipy